The brain is not a rigid organ, and its structures change by different kinds of experiences and diseases. Localization of structural brain changes on magnetic resonance imaging (MRI) scans is a laborious issue in psychiatric diseases [1, 2]. Many investigators have been using MRI scans as a tool for diagnosis of neurological diseases or tracking disease progression, etc. Therefore, to help them, automated methods have been replaced to identify brain changes without the need for time-consuming manual measurement, and have grown in popularity since their introduction.
One of these automated methods is voxel-based morphometry (VBM) introduced by Ashburner and Friston . This method is objective and able to perform a voxel-wise estimation to localize changes of a specific tissue. VBM commonly uses T1-weighted MRI scans and performs statistical tests across all voxels in the image to identify volume differences between groups. In VBM, there are three main preprocessing steps before statistical tests: segmentation, normalization, and smoothing.
The first step in preprocessing is segmentation. In this step, gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and other tissues are extracted. Once an original brain image is used, it is primarily corrected for inhomogeneity of the magnetic field which affects the intensity values of the image voxel. This correction is called bias correction. Another factor that should be well addressed is the partial volume effect. The effect can occur at the boundaries of the tissues whose intensity values overlap . By these corrections, the segmented tissue maps are produced.
To compare tissue-segmented images, the images must be normalized. Normalization ascertains that different brain sizes, different head positions, and somewhat different brain shapes of the subjects during MR imaging are corrected using linear and nonlinear normalizations although small differences still remain.
The final step of preprocessing is smoothing. In this step, the normalized segmented images are convolved with an isotropic Gaussian kernel. The output is a weighted average of each voxel’s neighborhood. The underlying reasons for using smoothing are an increase of normality of residuals and signal to noise ratio and decrease of effect of misregistration between images .
After preprocessing, statistical analysis is performed on the images. It can be parametric using general linear model  or nonparametric [6, 7]. A statistical test demonstrates alterations in tissue volume between subject groups to a user-selected p value. To remove false positives from the results, some methods such as family-wise error (FWE) correction or false discovery rate (FDR) correction could be applied [8, 9]. The final result is a statistical map localizing differences of a specified tissue between groups.
Three approaches of VBM include standard, optimized, and DARTEL (Diffeomorphic Anatomic Registration Through Exponentiated Lie algebra algorithm) [10,11,12]. The three approaches of VBM have been described in the literatures in detail [13, 14]. The difference between DARTEL and two first approaches is that using DARTEL, the high dimensional wrapping process was performed . Therefore, misregistration and inaccuracies are reduced more between the template and individual images as well as credibility of the research is increased [15, 16].
The other method is an automated ROI analysis . To perform this analysis, probabilistic brain atlases are employed. Probabilistic atlasing is a technique that generates anatomical templates and retains quantitative information on inter-subject variations across the population used to construct the atlas . Using these atlases, it may solve problems of manual ROI assessment and increase repeatability of studies. Examples of these atlases are hammers, lpba40, and neuromorphometrics which are described below.
The three atlases are created using a label-based approach and based on multiple subjects. They are created using manual tracing on anatomical MRI from healthy subjects. The individual subject classifications are then registered to MNI space to generate a probabilistic atlas. The hammers, lpba40, and neuromorphometrics are composed of 69, 40, and 140 regions, respectively. These regions cover the whole cortex and the main subcortical structures. The probabilistic brain atlases have been detailed in the literatures [19,20,21].
More recently, the abovementioned automatic methods are being increasingly applied to detect the brain volumetric alterations  in psychiatric diseases such as Alzheimer’s disease [23, 24], epilepsy , Parkinson’s disease , and bipolar disorder [27, 28].
In this regard, Lagopoulos et al. found that there were potential changes in the WM content of the corpus callosum of BP I patients in the early stage of the disease using structural MRI and DTI and FSL software . Several investigations indicated the WM and GM changes in different parts of BP patients’ brains including the amygdala, hippocampus, and temporal and frontal lobes [30, 31]. Also, Mahon et al. proposed that deficits in dorsolateral prefrontal and limbic cortical structures were the main manifestations of BP disorder .
The present study had three objectives. The primary aim was to apply DARTEL VBM to detect structural GM changes in patients with BP I in comparison to the healthy group. The second aim was to compare the three probabilistic brain atlases, i.e., hammers, lpba40, and neuromorphometrics atlases. The final aim of this study was to assess these methods, i.e., VBM versus ROI analyses. It is hypothesized that a VBM analysis of the same data would complement the ROI findings. In the present study, we used Computational Anatomy Toolbox (CAT12) which is an extension to the SPM12 software package (Statistical Parametric Mapping).